Foundations of Statistics and Probability for Machine Learning from Pluralsight

This course will teach you the concepts, theory, and implementation of basic statistics, probability, hypothesis testing, and regression analysis required to build and interpret meaningful machine learning models.

Learning the importance of p-values and test statistics and how these can be used to accept or reject the null hypothesis can lead you to explore the different types of t-tests and learn to choose the right one for your use case.

In this course, Foundations of Statistics and Probability for Machine Learning, you will learn to leverage statistics for exploratory data analysis and hypothesis testing.

First, you will explore measures of central tendency and dispersion including mean, mode, median, range, and standard deviation.

Then, you will explore the basics of probability and probability distributions and learn how skewness and kurtosis can give you important insights into your data.

Next, you will discover how you can perform hypothesis testing and interpret the results of these statistical tests.

Finally, you will learn how to perform and interpret regression models both simple regression with a single predictor and multiple regression with multiple predictors, and you will evaluate your regression models using R-squared and adjusted R-squared and understand the t-statistic and p-value associated with regression coefficients.

When you are finished with this course, you will have the skills and knowledge of statistics and data analysis needed to effectively explore and interpret your data as a precursor to applying machine learning techniques.

What's inside

Syllabus

Course Overview

Understanding Descriptive Statistics and Probability Distributions

Interpreting Data Using Statistical Test

Performing Regression Analysis

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Suitable for beginners interested in theoretical underpinnings of statistical techniques in machine learning

Assumes a foundational understanding of statistics and probability

Covers basic statistical concepts, making it useful for those without extensive prior knowledge

Course taught by instructors who have expertise in statistics applied to machine learning

Reviews summary

Foundational statistics for machine learning

According to learners, this course offers a solid foundation in the statistical and probability concepts essential for machine learning. Students find it particularly strong in explaining descriptive statistics, probability distributions, and the interpretation of hypothesis testing. The segment on regression analysis, covering both simple and multiple models, is highlighted as practical and well-explained, helping learners evaluate models effectively. While it provides a clear entry point for beginners, some suggest that those with prior knowledge might find the depth limited, wishing for more advanced topics or hands-on coding opportunities. Overall, it serves as an excellent precursor for applying ML techniques.

Delivers a clear understanding of regression models and their evaluation.

"The segment on simple and multiple regression was well-explained, making it easy to understand the models."

"Learning to interpret R-squared and the significance of regression coefficients was very practical."

"I can now confidently perform regression analysis and evaluate its effectiveness."

Directly links statistical theory to machine learning applications.

"I appreciated how the course constantly tied statistical principles back to their importance in building and interpreting ML models."

"The focus on how statistics preps you for machine learning was super valuable; it's not just abstract math."

"Understanding p-values and hypothesis testing for ML use cases was a key takeaway for me."

Provides essential statistical concepts for machine learning.

"This course helped me grasp the basic statistical concepts that are truly foundational for understanding machine learning algorithms."

"I found the explanations of central tendency, dispersion, and probability distributions very clear and to the point."

"It covers exactly what I needed to know about statistics to prepare for more advanced ML topics."

May require a certain baseline of mathematical understanding.

"Some parts were challenging without a strong grasp of underlying mathematical concepts, so be prepared to review."

"I struggled a bit with the more theoretical sections without a stronger math background, but it's manageable."

"While foundational, having some prior exposure to basic algebra helps immensely with understanding the formulas."

Suitable for beginners, but may lack advanced or practical depth.

"As someone new to statistics, I found the pace just right for building a solid foundation."

"The course felt a bit too basic for my existing knowledge; I was hoping for more advanced statistical techniques."

"I wish there were more hands-on coding exercises to apply these concepts, rather than just theory."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Foundations of Statistics and Probability for Machine Learning with these activities:

Review Probability Basics

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Refresh your understanding of basic probability concepts to ensure a solid foundation for the course.

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Review definitions of probability, sample space, and events.
Practice calculating probabilities using basic rules.
Solve simple probability problems.

Join a Statistics Study Group or Forum

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Enhance your comprehension by engaging in discussions and collaborating with peers in a study group or online forum.

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Join or create a study group for this course.
Participate actively in discussions, ask questions, and share your insights.
Review and provide feedback on others' work.

Hypothesis Testing Practice Exercises

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Reinforce your understanding of hypothesis testing through guided practice exercises.

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Complete practice problems on different types of hypothesis tests (e.g., t-test, chi-square test).
Apply hypothesis testing concepts to analyze real-world scenarios.
Evaluate the results of hypothesis tests and draw conclusions.

Four other activities

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Read 'Introduction to Statistical Learning' by Gareth James et al.

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Expand your knowledge of statistical learning concepts by delving into this comprehensive textbook.

View An Introduction to Statistical Learning: with... on Amazon

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Read the assigned chapters and complete the exercises.
Summarize the key concepts covered in each chapter.
Apply the concepts to real-world examples.

Explore Regression Analysis with Step-by-Step Tutorials

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Expand your knowledge of regression analysis by following detailed tutorials and working through examples.

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Follow tutorials on different regression methods (e.g., linear regression, logistic regression).
Apply regression techniques to real-world datasets.
Interpret regression results and make predictions.

Create an Infographic on a Statistical Concept

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Enhance your understanding and communication skills by creating a visually appealing infographic on a statistical concept.

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Choose a statistical concept that you want to illustrate.
Research and gather data on the concept.
Design an infographic that presents the information in a clear and engaging way.
Share your infographic with others and get feedback.

Contribute to an Open-Source Statistical Library

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Enhance your coding and statistical skills by contributing to open-source projects related to statistical analysis.

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Identify a suitable open-source statistical library.
Familiarize yourself with the codebase and documentation.
Identify areas where you can contribute, such as bug fixes or feature enhancements.
Submit a pull request with your contributions.
Collaborate with the project maintainers to improve your contributions.

Career center

Learners who complete Foundations of Statistics and Probability for Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist is someone who uses data to solve problems. They use a variety of statistical and analytical tools to uncover insights from data and make predictions about the future. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Data Scientist by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively explore and interpret data, build machine learning models, and make informed decisions.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer is someone who builds and deploys machine learning models. They use a variety of statistical and analytical techniques to train and test models to solve real-world problems. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Machine Learning Engineer by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively build and deploy machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst is someone who uses data to identify trends and patterns. They use a variety of statistical and analytical tools to explore data and uncover insights. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Data Analyst by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively explore and interpret data, identify trends and patterns, and communicate your findings.

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Statistician

A Statistician is someone who uses statistics to design and conduct surveys, experiments, and other studies. They use a variety of statistical and analytical techniques to collect, analyze, and interpret data. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Statistician by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively design and conduct studies, collect, analyze, and interpret data, and communicate your findings.

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Quantitative Analyst

A Quantitative Analyst is someone who uses mathematical and statistical models to analyze financial data. They use these models to make investment decisions and develop trading strategies. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Quantitative Analyst by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively analyze financial data, make investment decisions, and develop trading strategies.

See salaries and explore the career path for Quantitative Analyst

Actuary

An Actuary is someone who uses mathematical and statistical techniques to assess risk and uncertainty. They use these techniques to develop insurance policies and other financial products. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for an Actuary by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively assess risk and uncertainty, and develop insurance policies and other financial products.

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Biostatistician

A Biostatistician is someone who uses statistical methods to design and analyze studies in the field of biology. They use these methods to investigate the causes and risk factors for diseases, and to develop new treatments and cures. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Biostatistician by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively design and analyze studies in the field of biology, investigate the causes and risk factors for diseases, and develop new treatments and cures.

See salaries and explore the career path for Biostatistician

Epidemiologist

An Epidemiologist is someone who studies the distribution and determinants of health-related states or events in specified populations. They use a variety of statistical and analytical techniques to investigate the causes of disease and other health problems, and to develop strategies to prevent and control these problems. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for an Epidemiologist by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively investigate the causes of disease and other health problems, and develop strategies to prevent and control these problems.

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Data Architect

A Data Architect is someone who designs and builds data systems. They use a variety of statistical and analytical techniques to ensure that data is accurate, reliable, and accessible. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Data Architect by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively design and build data systems, ensure that data is accurate, reliable, and accessible, and meet the needs of your organization.

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Market Researcher

A Market Researcher is someone who studies the market for a particular product or service. They use a variety of statistical and analytical techniques to collect and analyze data about consumer behavior. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Market Researcher by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively collect and analyze data about consumer behavior, identify trends and patterns, and make recommendations to your clients.

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Business Analyst

A Business Analyst is someone who analyzes business processes and systems to identify areas for improvement. They use a variety of statistical and analytical techniques to collect and analyze data, and to make recommendations for change. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Business Analyst by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively collect and analyze data, identify areas for improvement, and make recommendations for change.

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Financial Analyst

A Financial Analyst is someone who analyzes financial data to make investment recommendations. They use a variety of statistical and analytical techniques to assess the risk and return of different investments. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Financial Analyst by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively analyze financial data, assess the risk and return of different investments, and make investment recommendations.

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Risk Manager

A Risk Manager is someone who identifies and manages risks for an organization. They use a variety of statistical and analytical techniques to assess the likelihood and impact of different risks, and to develop strategies to mitigate these risks. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for a Risk Manager by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively identify and manage risks for an organization.

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Operations Research Analyst

An Operations Research Analyst is someone who uses mathematical and statistical techniques to solve problems related to the operations of an organization. They use these techniques to improve efficiency, productivity, and profitability. The Foundations of Statistics and Probability for Machine Learning course provides a strong foundation for an Operations Research Analyst by teaching you the basic concepts of statistics, probability, hypothesis testing, and regression analysis. This course will help you develop the skills you need to effectively solve problems related to the operations of an organization, improve efficiency, productivity, and profitability.

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Teacher

A Teacher is someone who teaches students at a school or other educational institution. They use a variety of teaching methods to help students learn new concepts and skills. The Foundations of Statistics and Probability for Machine Learning course may be useful for a Teacher who wants to teach statistics or probability at a high school or college level. This course will help you develop the skills you need to effectively teach statistics and probability, and to help your students learn these important concepts.

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